add code for testing databricks access outside of databricks

app.py

@@ -1,20 +1,92 @@
 import streamlit as st
-from transformers import pipeline
-from PIL import Image
+import os
+from mlflow import deployments
+from databricks.vector_search.client import VectorSearchClient
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+DATABRICKS_HOST = os.environ.get("DATABRICKS_HOST")
+DATABRICKS_API_TOKEN = os.environ.get("DATABRICKS_API_TOKEN")
+VS_ENDPOINT_NAME = os.environ.get("VS_ENDPOINT_NAME")
+VS_INDEX_NAME = os.environ.get("VS_INDEX_NAME")
 
-st.title("Hot Dog? Or Not?")
+if DATABRICKS_HOST is None:
+    raise ValueError("DATABRICKS_HOST environment variable must be set")
+if DATABRICKS_API_TOKEN is None:
+    raise ValueError("DATABRICKS_API_TOKEN environment variable must be set")
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+TITLE = "VUMC Chatbot"
+DESCRIPTION="The first generation VUMC chatbot with knowledge of Vanderbilt specific terms."
+EXAMPLE_PROMPTS = [
+    "Write a short story about a robot that has a nice day.",
+    "In a table, what are some of the most common misconceptions about birds?",
+    "Give me a recipe for vegan banana bread.",
+    "Code a python function that can run merge sort on a list.",
+    "Give me the character profile of a gumdrop obsessed knight in JSON.",
+    "Write a rap battle between Alan Turing and Claude Shannon.",
+]
 
-if file_name is not None:
-    col1, col2 = st.columns(2)
+st.set_page_config(layout="wide")
+st.title(TITLE)
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
+# test env vars get output correctly - do we need to configure PAT access further?
+st.markdown(DESCRIPTION)
+st.markdown("\n")
+st.write(DATABRICKS_HOST)
+st.markdown("\n")
+st.write(VS_ENDPOINT_NAME)
+st.markdown("\n")
 
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+# use this to format later
+# with open("style.css") as css:
+#     st.markdown( f'<style>{css.read()}</style>' , unsafe_allow_html= True)
+
+vsc = VectorSearchClient()
+
+question = "What is the data lake?"
+# question_2 = "What does EDW stand for?"
+# question_3 = "What does AIDET stand for?"
+
+deploy_client = deployments.get_deploy_client("databricks")
+response = deploy_client.predict(endpoint="databricks-bge-large-en", inputs={"input": [question]})
+embeddings = [e['embedding'] for e in response.data]
+
+results = vsc.get_index(VS_ENDPOINT_NAME, VS_INDEX_NAME).similarity_search(
+  query_vector=embeddings[0],
+  columns=["name", "description"],
+  num_results=5)
+
+st.write(results)
+
+
+# print(results)
+# print("---------------------------------------")
+# vumc_terms = results.get('result', {}).get('data_array', [])
+# print(vumc_terms)
+
+
+# DBRX mainbody minus functions
+
+# main = st.container()
+# with main:
+#     history = st.container(height=400)
+#     with history:
+#         for message in st.session_state["messages"]:
+#             avatar = None
+#             if message["role"] == "assistant":
+#                 avatar = MODEL_AVATAR_URL
+#             with st.chat_message(message["role"],avatar=avatar):
+#                 if message["content"] is not None:
+#                     st.markdown(message["content"])
+#                 if message["error"] is not None:
+#                     st.error(message["error"],icon="🚨")
+#                 if message["warning"] is not None:
+#                     st.warning(message["warning"],icon="⚠️")
+
+#     if prompt := st.chat_input("Type a message!", max_chars=1000):
+#         handle_user_input(prompt)
+#     st.markdown("\n") #add some space for iphone users
+
+# with st.sidebar:
+#     with st.container():
+#         st.title("Examples")
+#         for prompt in EXAMPLE_PROMPTS:
+#             st.button(prompt, args=(prompt,), on_click=handle_user_input)

style.css

@@ -0,0 +1,3 @@
+.st-emotion-cache-1tpusnk a{
+    color: #FF5F46;
+}